Title

"Ising model on random networks and canonical tensor model"

Speaker

Yuki Sato (Univ. of the Witwatersrand)

Abstract

We introduce a statistical system on random networks for the purpose of studying the canonical tensor model, which is a theory of dynamical fuzzy spaces. The partition function of the statistical system has a concise expression in terms of integrals, and has the same symmetries as those of the canonical tensor model. We especially consider the Ising model on random networks, and find that its phase diagram agrees with what is implied by regrading the Hamiltonian vector field of the canonical tensor model with N = 2 where N is a cardinality of tensor indices. This is a new example connecting a model of quantum gravity and a random statistical system.

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